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During the transition from conventional towards purely electrical, sustainable mobility, transitional technologies play a major part in the task of increasing adaption rates and decreasing range anxiety. Developing new concepts to meet this challenge requires adaptive test benches, which can easily be modified e.g. when progressing from one stage of development to the next, but also meet certain sustainability demands themselves.
The system architecture presented in this paper is built around a service-oriented software layer, connecting a modular hardware layer for direct access to sensors and actuators to an extensible set of client tools. Providing flexibility, serviceability and ease of use, while maintaining a high level of reusability for its constituent components and providing features to reduce the required overall run time of the test benches, it can effectively decrease the CO2 emissions of the test bench while increasing its sustainability and efficiency.
This paper presents a cascaded methodology for enhancing the path accuracy of industrial robots by using advanced control schemes. It includes kinematic calibration as well as dynamic modeling and identification. This is followed by a centralized model-based compensation of robot dynamics. The implemented feed-forward torque control shows the expected improvements of control accuracy. However, external measurements show the influence of joint elasticities as systematic path errors. To further increase the accuracy an iterative learning controller (ILC) based on external camera measurements is designed. The implementation yields to significant improvements of path accuracy. By means of a kind of automated ”Teach-In”, an overall effective concept for the automated calibration and optimization of the accuracy of industrial robots in high-dynamic path-applications is realized.